Apache Pinot™ was originally designed as an append-only OLAP (online analytical processing) database. After some redesign, it was modified to support upserts, which are UPdates plus inSERTs. This…

In the realm of fintech, settlements, refunds, and chargebacks related to PSPs (payment service providers) are crucial transactions that involve the transfer of funds between different financial…

Generative AI powered by LLMs (Large Language Models) has a wide range of applications at Uber, like Uber Eats recommendations and search, customer support chatbots, code development, and SQL query…

In today’s fast-paced tech environment, maintaining robust on-call operations is crucial for ensuring seamless service functioning. Modern platform engineering teams face the challenge of efficiently managing on-call schedules, incident response, communication during critical moments, and strong customer support on Slack® channels.

This post describes Genie, an on-call copilot we built that uses generative AI to optimize communication and question-answering with on-call engineers.

Learn how Uber was able to reduce evaluation latencies by a factor of 100x in their Experimentation platform, which is used to empower decision making across the company by processing over 10 million…

Presto™ is an open source SQL query engine used on a large scale at Uber. Uber has around 20+ Presto clusters comprising over 12,000 hosts. We have about 7,000 weekly users and run about half a million queries per day. Presto has various use cases at Uber like ad hoc interactive analytics, ETL and batch workloads, dashboarding, data quality checks, report generation, experimentation, and data-driven services. Due to the scale of the system, there are various opportunities to make it more efficient. However, these opportunities need intelligence regarding the queries being processed by the system.

Learn how Uber made a breakthrough in tracking design metrics across Figma, Android, and iOS with Design System Observability.

SQL is a vital tool used daily by engineers, operations managers, and data scientists at Uber to access and manipulate terabytes of data. Crafting these queries not only requires a solid understanding of SQL syntax, but also deep knowledge of how our internal data models represent business concepts. QueryGPT aims to bridge this gap, enabling users to generate SQL queries through natural language prompts, thereby significantly enhancing productivity.

QueryGPT uses large language models (LLM), vector databases, and similarity search to generate complex queries from English questions that are provided by the user as input.

This article chronicles our development journey over the past year and where we are today with this vision.

In the competitive world of corporate travel, efficiency and precision are paramount. At Uber, we’ve recognized a specific need within this sector: senior executives and their assistants require a streamlined and reliable booking experience. To address this, we developed the Delegate Booking tool, a significant enhancement to our platform designed to empower executive assistants (EAs) to manage travel with ease and precision.

Learn how Uber is streamlining the Cloud migration of its massive Data Lake by incorporating key Data Mesh principles.

In the dynamic ecosystem of Uber, search functionality serves as the backbone for numerous critical operations, ranging from matching riders to drivers, to geo search functionalities within Uber ride apps, to facilitating seamless exploration of restaurants and dishes in Uber Eats. The reliance on search is paramount, given the diverse and extensive nature of Uber’s service offerings.

The Search Platform team at Uber has built an in-house search engine on top of Apache Lucene, with the primary objective of establishing a unified search infrastructure across all business verticals. Since its inception in 2019, the service has operated on Lucene version 7.5.0, which is two major versions and nearly four years behind the latest iteration.

Learn how Uber uses Apache Pinot for serving over 100 low-latency offline analytics use cases.

In this blog, we share how we reimagined CD at Uber to improve deployment automation and UX of managing microservices, while tackling the peculiar challenges of working with large monorepos.

Learn how we achieved diff-time E2E testing for thousands of microservices at Uber.

Discover how Uber's in-house ETL framework helps standardize modular ETL development, improving developer productivity and ensuring reliable data pipelines.

Learn all about our journey of successfully upgrading our MySQL fleet at Uber from v5.7 to v8.0, enhancing performance and reliability.